This invention relates generally to methods and systems for diagnosing or monitoring progress of a pathology and, more particularly, to methods and systems for diagnosing or monitoring progress of a pathology using laser induced breakdown spectroscopy (LIBS) and machine learning.
Outcomes are significantly improved if a pathology is detected early. Early detection can be related to noninvasive monitoring since patients are more likely to be monitored. A “liquid biopsy” provides a noninvasive path for determining an early diagnosis or monitoring progress for pathology.
One significant example is the early diagnosis or monitoring progress of cancer. Cancer indicates a class of diseases related to abnormal cell growth in one organ or tissue, with the potential to spread to other parts of the body. Many research and medical efforts are ongoing to efficiently diagnose and fight cancer, but the various forms of this disease are still one of the leading causes of death worldwide. Fighting cancer is very complex, in that it necessarily involves many different aspects, such as: investigating the causes of its onset; developing minimally invasive, targeted, and, in the future, personalized therapy approaches; promoting prevention practices; implementing screening tests for early diagnosis. The latter is a key task, as it is well-documented that detecting the onset of the disease during its early stage of development can significantly improve significantly the success of treatments and ultimately the survival rate and quality of life of patients. This issue is particularly critical for kinds of cancer that develop in the absence of specific symptoms and can go largely unnoticed until they metastasize, such as epithelial ovarian cancer (EOC), pancreatic cancer, and melanoma.
Developing large-scale screening tests is one of the most efficient strategies for early diagnosis of this kind of tumors. Ideally, such tests should be rapid and minimally invasive, user-friendly, accurate (low number of false positives and false negatives), and easy to integrate in point-of-care structures, so as to reach and monitor large numbers of people on a periodic basis. Laser-Induced Breakdown Spectroscopy (LIBS) is characterized by well-known practical advantages, which include limited sample preparation, fast multi-elemental response, compact instrumentation, possibility of in situ analyses, and versatility, all of which can contribute to making this technique a powerful tool in the fight against cancer
Despite being essentially an atomic spectroscopy technique, and as such not an obvious choice for the diagnosis of diseases that proceed through an abnormal proliferation of cells, LIBS has proved useful to distinguish between biopsied cancerous tissues and adjacent healthy ones, thanks to differences in the content of trace elements. In particular, previous studies have almost consistently shown that cancerous lesions have a different alkaline and alkaline earth metals content than healthy tissues.
In N. Melikechi, Y. Markushin, D. C. Connolly, J. Lasue, E. Ewusi-Annan, S. Makrogiannis, Spectrochim. Acta B 123 (2016) 33, it was proposed for the first time to develop a LIBS-based “liquid biopsy” approach for the early detection of cancer, i.e. the analyzed samples were not tissues (either biopsied or harvested from laboratory animals), but sera. Femtosecond-LIBS spectra of mice sera taken from animals with EOC and healthy controls of three different age groups were acquired and deposited on a solid substrate. The LIBS spectra were then fed to two different classification algorithms that were shown to be useful for the discrimination of sera from mice with cancer and healthy ones with a maximum accuracy around 80%. Recently, Chen et al. have adopted essentially the same experimental and computational approach to the diagnosis of lymphoma and multiple myeloma in human serum, and have obtained classification accuracies close to 100% (X. Chen, X. Li, X. Yu, D. Chen, A. Liu, Spectrochim. Acta B 139 (2018) 63).
There is a need for systems and methods for diagnosing or monitoring progress of a pathology using laser induced breakdown spectroscopy (LIBS) and machine learning.